Privacy Policy Inference of User-Uploaded Images on social network

Computer Engineering

Social Network is an emerging e-service for content sharing sites (CSS).With the increasing large no. of images users share through social sites, has maintaining privacy and protection has become a major problem, as demonstrated by a recent wave of incidents where users carelessly shared personal information. Social Networking Sites (SNS) such as Facebook and MySpace have engage millions of users because of their ability to connect individuals by social graphs and status. In light of these events, the need of tools and protector to help users control access to their shared image content is likely. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system and NLP processing to help users compose privacy settings for their images. We examine the role of social context, image content, and metadata as possible indicators of users’ privacy preferences. We propose a novel approach of grouping friends to improve the privacy policy also we propose a two-level framework which according to the user’s available history on the site determines the best available privacy policy for the user’s images being uploaded. The Framework determines the best privacy policy for the uploaded images. It includes an Image classification framework for association of images with similar policies and a policy prediction technique to automatically generate a privacy policy for user-uploaded images. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the generated policies will follow the evolution of users’ privacy attitude. We provide the results of our extensive evaluation over 6,000 policies, which demonstrate the effectiveness of our system, with prediction accuracies over 80 percent

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